{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第五步：调整正则化参数：reg_alpha 和reg_lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import math\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取训练数据\n",
    "train = pd.read_csv(\"RentListingInquries_FE_train.csv\")\n",
    "# 取出特征与标签\n",
    "y_train = train[\"interest_level\"].values\n",
    "train = train.drop([\"interest_level\"], axis=1)\n",
    "X_train = np.array(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare cross validation\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reg_alpha': [1.5, 2], 'reg_lambda': [0.5, 1, 2]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reg_alpha = [1e-3, 1e-2, 0.05, 0.1]    #default = 0\n",
    "#reg_lambda = [1e-3, 1e-2, 0.05, 0.1]   #default = 1\n",
    "reg_alpha = [ 1.5, 2]    #default = 0, 测试0.1,1，1.5，2\n",
    "reg_lambda = [0.5, 1, 2]      #default = 1，测试0.1， 0.5， 1，2\n",
    "\n",
    "param_test5 = dict(reg_alpha=reg_alpha, reg_lambda=reg_lambda)\n",
    "param_test5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb5  = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=262,  #第三步调整得到的新n_estimators最优值\n",
    "        max_depth=5,\n",
    "        min_child_weight=1, #第二步调整得到的最小叶子节点权重和和最大深度\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.9, #第四步调整得到的两个最优采样比例\n",
    "        colsample_bylevel = 0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3,\n",
    "        silent=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
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      "[07:40:41] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[07:40:41] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[07:40:42] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[07:40:42] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[07:40:43] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.58181, std: 0.00330, params: {'reg_alpha': 1.5, 'reg_lambda': 0.5},\n",
       "  mean: -0.58177, std: 0.00385, params: {'reg_alpha': 1.5, 'reg_lambda': 1},\n",
       "  mean: -0.58166, std: 0.00365, params: {'reg_alpha': 1.5, 'reg_lambda': 2},\n",
       "  mean: -0.58178, std: 0.00318, params: {'reg_alpha': 2, 'reg_lambda': 0.5},\n",
       "  mean: -0.58178, std: 0.00356, params: {'reg_alpha': 2, 'reg_lambda': 1},\n",
       "  mean: -0.58183, std: 0.00356, params: {'reg_alpha': 2, 'reg_lambda': 2}],\n",
       " {'reg_alpha': 1.5, 'reg_lambda': 2},\n",
       " -0.5816557058399655)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch5 = GridSearchCV(xgb5, param_grid = param_test5, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gsearch5.fit(X_train , y_train)\n",
    "\n",
    "gsearch5.grid_scores_, gsearch5.best_params_,     gsearch5.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([  582.06159997,   554.99499998,   558.46299996,   559.18660011,\n",
       "         4818.59500003, 13315.76719999]),\n",
       " 'mean_score_time': array([0.89239998, 0.82059999, 0.802     , 0.79879999, 0.82679996,\n",
       "        0.83919997]),\n",
       " 'mean_test_score': array([-0.58180778, -0.58176835, -0.58165571, -0.58178351, -0.58178448,\n",
       "        -0.58183093]),\n",
       " 'mean_train_score': array([-0.49456342, -0.49617578, -0.4987674 , -0.49776805, -0.49880214,\n",
       "        -0.50080016]),\n",
       " 'param_reg_alpha': masked_array(data=[1.5, 1.5, 1.5, 2, 2, 2],\n",
       "              mask=[False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_reg_lambda': masked_array(data=[0.5, 1, 2, 0.5, 1, 2],\n",
       "              mask=[False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'reg_alpha': 1.5, 'reg_lambda': 0.5},\n",
       "  {'reg_alpha': 1.5, 'reg_lambda': 1},\n",
       "  {'reg_alpha': 1.5, 'reg_lambda': 2},\n",
       "  {'reg_alpha': 2, 'reg_lambda': 0.5},\n",
       "  {'reg_alpha': 2, 'reg_lambda': 1},\n",
       "  {'reg_alpha': 2, 'reg_lambda': 2}],\n",
       " 'rank_test_score': array([5, 2, 1, 3, 4, 6]),\n",
       " 'split0_test_score': array([-0.57643829, -0.57554412, -0.57586603, -0.57657008, -0.57626711,\n",
       "        -0.57622865]),\n",
       " 'split0_train_score': array([-0.49533306, -0.49659826, -0.49933804, -0.49831045, -0.49960075,\n",
       "        -0.50169044]),\n",
       " 'split1_test_score': array([-0.57968652, -0.57940588, -0.57930967, -0.57973041, -0.5792127 ,\n",
       "        -0.57948143]),\n",
       " 'split1_train_score': array([-0.49459458, -0.4970486 , -0.49875173, -0.49816277, -0.49913195,\n",
       "        -0.50012329]),\n",
       " 'split2_test_score': array([-0.58301252, -0.58307836, -0.58276513, -0.58331456, -0.58276907,\n",
       "        -0.58261364]),\n",
       " 'split2_train_score': array([-0.49382501, -0.49579373, -0.49856265, -0.49801338, -0.49868612,\n",
       "        -0.50010119]),\n",
       " 'split3_test_score': array([-0.58490359, -0.58452983, -0.58430349, -0.58430855, -0.58509766,\n",
       "        -0.58536989]),\n",
       " 'split3_train_score': array([-0.4948996 , -0.49613367, -0.49917935, -0.49737054, -0.49875796,\n",
       "        -0.50136181]),\n",
       " 'split4_test_score': array([-0.58499893, -0.58628495, -0.58603554, -0.58499494, -0.58557703,\n",
       "        -0.58546216]),\n",
       " 'split4_train_score': array([-0.49416486, -0.49530463, -0.49800521, -0.49698312, -0.49783395,\n",
       "        -0.50072409]),\n",
       " 'std_fit_time': array([1.31762373e+01, 4.68287945e+00, 4.35582774e+00, 3.46627235e+00,\n",
       "        8.53455084e+03, 1.04978397e+04]),\n",
       " 'std_score_time': array([0.09357901, 0.05114921, 0.00777171, 0.00611222, 0.02206719,\n",
       "        0.16843211]),\n",
       " 'std_test_score': array([0.00330304, 0.00384869, 0.00364546, 0.00317579, 0.00356047,\n",
       "        0.00355607]),\n",
       " 'std_train_score': array([0.00053112, 0.00060777, 0.00047296, 0.00050682, 0.00058298,\n",
       "        0.00064196])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch5.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.581656 using {'reg_alpha': 1.5, 'reg_lambda': 2}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (gsearch5.best_score_, gsearch5.best_params_))\n",
    "test_means = gsearch5.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch5.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch5.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch5.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch5.cv_results_).to_csv('my_preds_reg_alpha_reg_lambda_1.csv')\n",
    "\n",
    "# plot results\n",
    "test_scores = np.array(test_means).reshape(len(reg_alpha), len(reg_lambda))\n",
    "train_scores = np.array(train_means).reshape(len(reg_alpha), len(reg_lambda))\n",
    "\n",
    "#log_reg_alpha = [0,0,0,0]\n",
    "#for index in range(len(reg_alpha)):\n",
    "#   log_reg_alpha[index] = math.log10(reg_alpha[index])\n",
    "    \n",
    "for i, value in enumerate(reg_alpha):\n",
    "    pyplot.plot(reg_lambda, test_scores[i], label= 'reg_alpha:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'reg_lambda' )                                                                                                      \n",
    "pyplot.ylabel( '-Log Loss' )\n",
    "pyplot.savefig( 'reg_alpha_vs_reg_lambda1.png' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'reg_alpha': [0.1, 1], 'reg_lambda': [1, 2]}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#进一步调整\n",
    "reg_alpha = [0.1,1]    \n",
    "reg_lambda = [1,2]       \n",
    "\n",
    "param_test5_2 = dict(reg_alpha=reg_alpha, reg_lambda=reg_lambda)\n",
    "param_test5_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14:35:21] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:22] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:22] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:23] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:23] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:24] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:24] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:25] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:25] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:26] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:26] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:27] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:27] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:28] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:28] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:29] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:29] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:30] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:30] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:31] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:31] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:32] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[14:35:32] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 50 extra nodes, 0 pruned nodes, max_depth=5\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.58175, std: 0.00412, params: {'reg_alpha': 0.1, 'reg_lambda': 1},\n",
       "  mean: -0.58252, std: 0.00365, params: {'reg_alpha': 0.1, 'reg_lambda': 2},\n",
       "  mean: -0.58166, std: 0.00371, params: {'reg_alpha': 1, 'reg_lambda': 1},\n",
       "  mean: -0.58221, std: 0.00376, params: {'reg_alpha': 1, 'reg_lambda': 2}],\n",
       " {'reg_alpha': 1, 'reg_lambda': 1},\n",
       " -0.5816601583842772)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch5_2 = GridSearchCV(xgb5, param_grid = param_test5_2, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gsearch5_2.fit(X_train , y_train)\n",
    "\n",
    "gsearch5_2.grid_scores_, gsearch5_2.best_params_,     gsearch5_2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.581660 using {'reg_alpha': 1, 'reg_lambda': 1}\n"
     ]
    },
    {
     "data": {
      "image/png": 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Y7H0+GJh47AoiUk1Eynqf1wS6A6nAXqCKiDT3Vu0NpBXxeluAc0QkTkTigXOK8ZiQkr/WZ39mNgNGz+O2CYvCs9anMCLQ8Ey4aizctRLOedDdTvydq+DFjvDdi3Boj99TGmMCwK8toBrA+0ASLhyuUtU9ItIZuEVVh3hnrr0K5OKC8jlVfcN7/OXACO97e4EbVXWjiHQBPgaqAZnALlVNFpFYYBTuLDjFbTHdU9ScobQFlF9mVg6jv9rIqNkbyFVl2NmNuTXca30Kk30UVk9yu+e2fAdx5eC0K901RXXb+T2dMeYYIb0LLlyEagDl2ZlxmCenrmbi0h3UqZzAQ31a0q9dvcg5bbsgu1bCwtGw/H3IOgQNTne751r3h7gyfk9njMECKCBCPYDyHFvr81jf1rRNrFr0A8PZ4Z/dmXMLR7tbileoBZ0GQ6c/QZX6fk9nTFSzAAqAcAkg8Gp9Fm/j6WlrSD94hCs7JnL/RRFS61OY3FzYONNV/qz9HCQGWl7ids816mHXFBnjAwugAAinAMqzPzOLl2at581vvqdsXCx/PrcpN3RvRNm4KLiN9t5NXhHqeDi8F05p6YKo7TVQNgIu5DUmTFgABUA4BlCe7386yBOfpTIjLUJrfQqTdRhWfuRO5d65FMpUgvbXulbuU5oX/XhjTIlYAAVAOAdQnjlrd/PPyams//EAPZrV5NFLW9OsdpRsDajC9kUuiFZ9DDlH4dRzXNNC84sgNkLPGjTGZxZAARAJAQSu1ufteZt5dvpaDkZyrU9hDuz+tQh13zaonAhdbnRFqBVq+j2dMRHFAigAIiWA8kRNrU9hcrJh7TS3VfT9HIgtA8lXuGNF9TvZSQvGBIAFUABEWgDlSd2xj39MWhX5tT5F2b3GK0J9F47uh3od3DVFba6A+HJ+T2dM2LIACoBIDSBwN5OaunIXT3yWxvafD9PntDo8dHErGlSPwgbqI/th2XsujHavhnLVoOMg6HwTVGvo93TGhB0LoACI5ADKk7/WJ0eVmyO91qcwqrDpa3dN0erPQHPdyQpdh0LjXlaEakwxWQAFQDQEUJ6dGYd5aupqPvFqff56cUv6t4/wWp/CZGyHRWNg0Vg4uBuqN3Gncbe/FspFeMuEMSVkARQA0RRAeRZtdrU+y7dl0DGpKsP7JUd+rU9hso9A6qeu8mfrfIgvD22vdseK6rTxezpjQpIFUABEYwDBb2t9fjpwhKs6RUmtT1F2LnO751Z8ANmZkHSm2z3Xqi/ERtEp7cYUwQIoAKI1gPIcW+tzx7lN+VO01PoU5tAeWDrBnbSwdxNUrAOdbnAflev6PJwx/rMACoBoD6A8rtYnjRlpP0RfrU9hcnNh/Qx3TdH6GRAT67aGug6DpDPsmiITtSyAAsAC6LeiutanKOkbXBHqkrcgMwNqJXtFqFdDmQp+T2dMqbIACgALoN87ttZnYLeG3H1+lNX6FOboIXeMaOFo2LUCylaBDte5M+hqNPF7OmNKhQVQAFgAHd+eg0cZOX0N78yP4lqfwqi6s+YWjIbUiZCbBU3Oc1tFzS5wu+uMiVAWQAFgAVS0tJ2u1mfexiiv9SnM/h9g8ThIGQP7d0DVJNey0HEQlK/u93TGBJwFUABYABWPqjJt5S4e92p9Lm5Th7/1idJan8LkZLmGhYWvu8aFuARo8we3e65+R7+nMyZgLIACwALoxBxb6zOsR2Nu6xWltT5F+SHVBdGy9yDrINTv7HbPJV8OcWX9ns6YErEACgALoJNjtT4nIDPDhdCC0ZC+DsrX9IpQb4SqDfyezpiTYgEUABZAJWO1PidAFTbOdltFa6a4ZS36uN1zjXvaNUUmrFgABYAFUMnl5ir/W7yNp6zWp/h+3uJOWFg8Dg6lQ83mLojaDYCEyn5PZ0yRLIACwAIocPLX+pSJjeHP5zWzWp+iZGVC6ieuaWH7IihTEdpe444V1Wrl93TGHJcFUABYAAVe/lqfhl6tz/lW61O07Ytgweuw8n+QcwQa9XBB1OISiLWTPExosQAKAAug4Plq7W5GWK3PiTuYDkvGw8I3IWMLVKrnTljoNBgq1vJ7OmMAC6CAsAAKLqv1KYHcHFj3hds9t2EmxMRD8mXuPkUNutpJC8ZXFkABYAFUOo6t9bnnghYM6NKAuFi7BXax/LTenT239B04kgF12rrdc22uhDJ2MbApfRZAAWABVLqOrfV5tG9rzmxS0++xwseRA7DifXes6MdVkFAVOlwPXW6C6o39ns5EEQugALAAKn15tT5PTElj216r9TkpqrBlrts9lzbJ7a5r1tvtnmt6PsTYlqUJLgugALAA8k9mVg6vf72Rl2f9Wutza88mVChrZ3ydkH07YdFYWDQGDvwA1U51W0Ttr7MiVBM0FkABYAHkv10ZmTw5NY1Plu6gduWy/PXillzWvr6dtn2iso/C6kmu8mfLXIgrB6dd6Y4V1W3n93QmwlgABYAFUOg4ttbnsb7JtGtgtT4nZdcKd9LC8vch6xA0ON3dRrxVP4gr4/d0JgJYAAWABVBoObbW58pOiTxgtT4n7/DP7sy5haNhz0aoUAs63QCd/wSV6/k9nQljFkABYAEUmvZnZvHyrA28+c33xMeK1fqUVG4ubJzpds+t/RwkBlpe4raKGp1l1xSZE2YBFAAWQKFt008HedxqfQJr7yZIeRMWj4fDe+GUVtB1iOugK2tNFaZ4ihtAvpyPKSLVRWS6iKzz/qx2nPVyRGSp9/FpvuXnichib/k3ItLUW36PiKSKyHIR+VJEGuZ7zGDv9daJyODgv0sTbI1qVuD1wZ0Zf2NX4mNjGDo+hUFvLmDdD/v9Hi18VWsEvUfAPWnQf5S7Od5n98K/W8GUB2D3Wr8nNBHEly0gEXka2KOqT4rIX4FqqvpgAesdUNWKBSxfC/RX1TQRuQ3oqqo3iEgvYL6qHhKRW4GeqnqNiFQHUoDOgAKLgE6qurewOW0LKHxk5eQyYd5mRlqtT2CpekWor8GqjyHnKJx6jts91/wiK0I1BQrKFpCIxIhIIG5I0h8Y530+DrjsBB+vQN4cVYAdAKo6S1UPecvnAYne5xcC01V1jxc604GLTnJ2E4LiY2O4ofupzL6/FwO6NmD83E30fGYWb83bTHZOrt/jhS8RSOwMV7wGd6fCuY9A+gb473XwfDv4+t9w8Ce/pzRhqsgAEpF3RKSyiFQAUoE1InJ/CV+3tqruBPD+PF6Nb4KIpIjIPBHJH1JDgCkisg0YCDxZwGNvAqZ6n9cHtub73jZvmYkw1SuU4fHLTuOzv/SgRZ1KPPLJSi598Ru+22A/JEus4ilw9n1w5zK45m2o0QS+HAEjW8FHN8O2FLfFZEwxFWcLqLWq7sNtpUwBknA/9AslIjNEZGUBH/1PYL4kbzPuWuA5EWniLb8b6KOqicAYYOQxr309bnfbv/IWFfDcBf6fIiLDvNBL2b179wmMakJJq7qVeXdoN165viMHjmRz7ej53PLWIrbuOVT0g03hYuOgVV8Y/CncvsCdur36M3j9PBjdC5ZMgKzDfk9pwkCRx4BEZBXQHngHeElV54jIMlU96cunRWQN7vjMThGpC8xW1RZFPGYsMBmYA8xT1Sbe8iRgmqq29r4+H3gROEdVf/SWDfBe72bv61e913y3sNe0Y0CRwWp9SsGR/bDsPXeB6+7VUK46dBwInW+Cag2LfryJKIE8BvQqsAmoAHzlnVm2r2Tj8SmQdybaYGDisSuISDURKet9XhPojtsFuBeoIiLNvVV7A2neeh28efvlhY/nc+AC7zmrARd4y0wUSIiP5Y5zmzHrvp5cclpdXpq1nnP/PZuPl2wjN9d2GQVE2Uqu1ue2eTB4krt+6LuX3HGid/4I67901xsZk89JnQUnInGqmn3SLypSA3gftztvC3CVqu4Rkc7ALao6RETOxIVJLi4on1PVN7zHXw6M8L63F7hRVTeKyAzgNGCn91JbVLWf95gbgb95y59Q1TFFzWlbQJFp0ea9jJi0imXbMujg1fq0t1qfwMvY7kpQF42Fg7uhehPoMgTaXwvl7O87kgXsQlQRuRN3nGU/8DrQAfirqn4RiEFDmQVQ5Cqw1ufCFtSqbLU+AZd9BFI/dZU/W+dDfHloe7W7PUSdNn5PZ4IgkAG0TFXbiciFwO3AI8AYVe0YmFFDlwVQ5Du21ueOc5tx41lW6xM0O5e5yp8VH0B2JiSd6XbdteoLsXbNVqQIZAAtV9W2IvI87sD9xyKyRFU7BGrYUGUBFD02/XSQJ6akMT3V1fo83KcVvVvXtlqfYDm0B5a8DSlvuPqfinXc2XSdboDKdX0ezpRUIANoDO6amVOBdkAsLog6BWLQUGYBFH2+XrebEZNSWffjAXo0q8kjl7ameW3rQAua3FxYP8M1LayfDjHeKd5dh0HSGVaEGqYCGUAxuNOwN6rqz94JBPVVdXlgRg1dFkDRqaBan7vOb0bV8navnKBK3+CKUJe8BZkZUCvZ7Z5rezWUqeD3dOYEBLQNW0T6AWd7X85R1UklnC8sWABFtz0HjzJy+hremb+FKuXiueeCFgzo0oC4WF86fKPH0UPuGNHC0e7meWWrQIfr3Bl0NZoU/Xjju0BuAT0JdAEmeIsGACmq+lCJpwxxFkAGIG3nPkZMSmXuxnRa1qnEo5e25symNf0eK/KpurPmFoyG1ImQmwVNznNbRc0ugBg7USRUBfQkBKC9quZ6X8cCS1S1bUAmDWEWQCaPqvL5ql08/lka2/Ye5qLkOjx8SSsaVC/v92jRYf8PsHgcpIyB/TugapJrWeg4CMpX93s6c4xAB1BPVd3jfV0ddxKCBZCJOplZObzxzfe8NHM9OaoM7XEqt/VsarU+pSUny/XOLXwdNn0NcQnQ5g9u91z9iL8yJGwEMoAG4NqmZ+FKPc8GHlLV9wIxaCizADLHsysjk6emrebjJdupXbksf724Jf3b1Scmxs7aKjU/pLogWvYeZB2E+p3d7rnky92N9IxvAn0SQl3ccSAB5gMxqrqjxFOGOAsgUxSr9QkBmRkuhBaMhvR1UL6m2zXX+Uao2sDv6aJSQAOogCffoqpJJzVZGLEAMsWRm6t8tGQ7T01bze79R/hDx0QevMhqfUqdKmyc7YJorXcrsBZ93O65xj3tmqJSFOwA2qqqEf+rhQWQOREHjmTz0sz1VusTCn7e4k5YWDwODqVDzeaue67dHyEhEDd1NoWxLaAAsAAyJ8NqfUJIViakfuKaFrYvgjIVXQh1GQq1Wvo9XcQqcQCJyIsUfNdQAQarasT/GmEBZEoif63PWU1r8mhfq/Xx1fZFsOB1WPk/yDkCjXq4kxZaXOLu8moCJhABNLjAb3hUddxJzhY2LIBMSWXn5PJ2vlqf609P4u7eza3Wx08H02HJeFj4JmRsgUr13AkLnQZDxVp+TxcRgroLLlpYAJlA2XPwKM9OX8uE+ZupXC6ee3s3Z0DXJKv18VNuDqz7wu2e2zATYuIh+TK3e65BVztpoQQsgALAAsgE2upd+/jHp67Wp0XtSjzW12p9QsJP6901RUsnwJF9UKet2z3X5kooY20XJ8oCKAAsgEwwFFTr87c+rUiqYT/ofHfkAKx43x0r+nEVJFSFDtdDl5ugemO/pwsbFkABYAFkgimv1uflWevJzrVan5CiCpu/c43caZPc7rpmvd19ipqcBzG267QwgazieaGAxRm4RuyJJzlfWLAAMqVhV0YmT09bzUdLtlOrkqv1uay91fqEjH07YdFYWDQGDvwA1U51W0Ttr7Mi1OMIZAC9BrQEPvAW/QFYBTTA3aTurhLOGrIsgExpslqfEJd9FFZPck0LW+ZCXDk47Up3rKhuO7+nCymBDKCZwAWqmu19HQd8AfQGVqhq6wDMG5IsgExps1qfMLFrhTtpYfn7kHUIGpzuds+16gdxdop9IANoDdBVVTO8r6sA81W1pYgsUdUOAZk4BFkAGb8cOJLNy7PW88bXrtbn9nObcmP3U0mIt1qfkHL4Z1j6jjtWtGcjVKgFnW6Azn+CyvX8ns43gQygm4C/A7P59XYM/we8CwxX1ftLPG2IsgAyftucfpDHP3O1PknVy/P3S6zWJyTl5sLGmV4R6ucgMdBB7bWgAAAVcElEQVTyErdV1OisqLumKBi3Y+iKC6AF0XArBrAAMqHDan3CyN5NsPANWPIWHN4Lp7SCrkOg7TVQNjr+zQIdQP1wWz4Ac1R1UgnnCwsWQCaUZOfkMmH+FkZOX8uBI9lW6xPqsg673rkFr8HOZVCmErS/1t0e4pTmfk8XVIHcBfck7mZ0E7xFA3CnYD9U4ilDnAWQCUV7Dx7l2RlreXueq/W5p3dzrrVan9ClCttS3HGiVR9DzlF3f6IuQ6H5RRFZhBrIAFoOtFfVXO/rWGCJqrYNyKQhzALIhDKr9QlDB3a7exSljIF926BKA3fCQsfBUCFy/u0CHUA9VXWP93V1YLYFkDH+c7U+P/DElFS27jnMhcm1ebhPa6v1CXU52e6urQtGw/dzILYMJF/hTlpI7OT3dCUWyAAaADwJzOLXs+AeUtX3AjFoKLMAMuHCan3C2O41XhHqu3B0P9Tr4HbPtbkC4sv5Pd1JCcZZcF1wATQfiImGM+EsgEy4sVqfMHZkPyx7z4XR7tVQrjp0HAidb4JqDf2e7oTYLbkDwALIhKvFW/byj0mpLNv6M+0bVOWxvq3pkFTN77FMcajCpq/d7rnVn4HmupMVug6Fxr3Cogg12AG0VVUbnNRkYcQCyIQzq/WJABnbXQnqorFwcDdUb+JO425/LZQL3Z5A2wIKAAsgEwms1icCZB+B1E/dNUXbFkB8eWh7tTtWVKeN39P9TokDSEReBAr6pgCDVbVyyUYMfRZAJpJsTj/IE5+l8YVX6/PwJa24wGp9ws+Ope6aohUfQnYmJJ3pds+16gux8X5PBwQmgAYX9kBVHXeSs4UNCyATib5Z9xMjJq9i7Q8H6N60Bo9emkyLOtFRERNRDu2BJW9Dyhuu/qdiHXdNUacboFIdX0cLyi44EamjqrtKNFkYsQAykcpqfSJIbi6sn+F2z62fDjFx7rYQXYdC0hm+FKEGK4AWq2rHEk3GLxez/hdoBGwCrlbVvQWslwOs8L7coqr9vOXnAf8CYoADwA2qul5E7gGGANnAbuBGVd0sIu2B/wCVgRzgCVX9b1FzWgCZSGe1PhEmfQOkvOmKUDMzoHYbd9JC26uhTIVSGyNYARSQ+/+IyNPAHlV9UkT+ClRT1QcLWO+AqlYsYPlaoL+qponIbbj7Fd0gIr1w9yo6JCK34hocrhGR5oCq6joRqQcsAlqp6s+FzWkBZKLF6l37GDEple82pNO8dkUe65tMd6v1CV9HD8GKD9yxol0roGwV6HCdC6MaTYL+8sUNoBP9NWf0Sc5zrP5A3jGkccBlJ/h4xW3NAFQBdgCo6ixVPeQtnwckesvXquo67/MdwI/AKSc9vTERpmWdykwYcjqvXN+Jw1k5XPf6fG5+K4Ut6YeKfrAJPWXKQ6fBcPPXcOPn0Ky320X3Ykd46wpYMxVyc/ye8uROwy7xi4r8rKpV8329V1V/d5WciGQDS3G71J5U1U+85T2AT4DDwD6gm6ruO+axLwG7VPXxY5Z3xYVecl7B6vHYFpCJRr+p9clRhvQ4ldt7Wa1P2Nv/g1eE+ibs3wlVk1zLQsdBUL56QF8qqNcBFXOAGUBBp2I8DIwrZgDVU9UdItIYmAmcp6obROQj4ClVnS8i9wMtVHVIvsddD9wBnKOqR/Itr4u7s+tgVZ13nLmHAcMAkpKSOm3evPmE37sxkcBqfSJUTpZrWFgwGjZ/A3EJ0OYPbvdc/RIf4gdCIIAKfVGRNbjjMzvzQkFVWxTxmLHAZGAOME9Vm3jLk4Bpqtra+/p84EVc+PyY7/GVceHz/1T1g+LMaVtAxlitT0T7IdV1zy17D7IOQv3OrpE7+TKIK3vSTxusY0CB8imQd53RYGDisSuISDURKet9XhPoDqQCe4Eq3okFAL2BNG+9DsCrQL9jwqcM8DEwvrjhY4xxOiZV4+Nbz+TfV7Vj+8+HuXzUd9zz/lJ+2Jfp92impGq3hktHwr1pcPHT7sy5j4fByNYw8wnXSxdEfm0B1QDeB5KALcBVqrpHRDoDt6jqEBE5ExcmubigfE5V3/AefzkwwvveXtzp1hu93X6nATu9l9qiqv28XXJjgFX5xrhBVZcWNqdtARnzW/lrfeJihdt7NeWms6zWJ2KowsbZbvdcbDxcfXJ9AyG9Cy5cWAAZUzCr9YkCuTkQc3K/WIT6LjhjTBhrWKMCrw3qzNs3nU5CfAw3v7WI69+Yz5pd+/0ezQTKSYbPCb1E0F/BGBOxzmpWkyl/6cGI/sms3L6Pi5//ikcnruTnQ0f9Hs2EAQsgY0yJxMXGMOiMRsy+ryfXd2vI2/M20/OZ2Yyfu4nsnEIvtTNRzgLIGBMQ1SqUYUT/Nky5swet61bm0Ymr6PPC13y7/ie/RzMhygLIGBNQebU+rw60Wh9TOAsgY0zAiQgXJtdh+t3ncP+FLfh63U+cP3IOT09bzYEj2X6PZ0KEBZAxJmgS4mO5vVdTZt3Xk0vb1WXU7A2c+8xs/rdoG7m5dglItLMAMsYEXe3KCYy8uj0f3XYmdauW494PlnHFf75jyZbf3QbMRBELIGNMqbFaH5OfBZAxplTFxAh/6JTIrPt6clvPJkxetpNez8zm5Vnryczy/x41pvRYABljfFGxbBwPXNSSGfecw1lNa/Kvz9fQ+9k5TFu5C6sIiw4WQMYYXyXVKP9LrU+5+FhuedtqfaKFBZAxJiQcr9Zn70Gr9YlUFkDGmJCRv9ZnYLeGTJi/hZ7PzGbcd1brE4ksgIwxIadahTL8o38bpvylB8n1KvPYp1brE4ksgIwxIatFnUq/q/UZNj6FzekH/R7NBIAFkDEmpB1b6/PN+p/oPfIrnrJan7BnAWSMCQvH1vr8x2p9wp4FkDEmrOTV+nycr9bncqv1CUsWQMaYsNTBq/UZeXU7dubV+vzXan3CiQWQMSZsxcQIV3RMZGZerc9yq/UJJxZAxpiwl7/Wp0czq/UJFxZAxpiIkVSjPK8O7MyEIadTPj6OW95exHWvz2f1rn1+j2YKYAFkjIk43ZvW5LO/nMWI/sms2rGPPs9/bbU+IcgCyBgTkazWJ/RZABljIlr+Wp829X+t9flmndX6+M0CyBgTFVrUqcTbN7lan8ysXK5/Yz5DrdbHVxZAxpiokVfr88XdZ/PARS341mp9fGUBZIyJOgnxsdzW87e1Pr2emc2HVutTqiyAjDFRK3+tT/2q5bjPq/VZbLU+pcICyBgT9TokVeOjfLU+V1itT6mwADLGGH6t9Zl1X09u72W1PqXBAsgYY/KpUDaO+y+0Wp/SYAFkjDEFsFqf4LMAMsaYQuTV+vyzfzKpO12tzyOfWK1PIFgAGWNMEeJiYxiYr9bnnQVW6xMIFkDGGFNMVctbrU8gWQAZY8wJslqfwPAlgESkuohMF5F13p/VjrNejogs9T4+zbf8PBFZ7C3/RkSaesvvEZFUEVkuIl+KSMNjnq+yiGwXkZeC+w6NMZHueLU+T061Wp/i8msL6K/Al6raDPjS+7ogh1W1vffRL9/y/wDXqWp74B3g797yJUBnVW0LfAg8fczz/ROYE6g3YYwx+Wt9+rarxytzrNanuPwKoP7AOO/zccBlJ/h4BSp7n1cBdgCo6ixVPeQtnwck5j1ARDoBtYEvTnJmY4w5rtqVE/j31e345Pbuv9b6jPrWan0K4VcA1VbVnQDen7WOs16CiKSIyDwRyR9SQ4ApIrINGAg8WcBjbwKmAohIDPBv4P6iBhORYd5rpuzevbv478gYY4D2Dar+WuuTkckVo77j7v8uZVeG1focKy5YTywiM4A6BXzr4RN4miRV3SEijYGZIrJCVTcAdwN9VHW+iNwPjMSFUt5rXw90Bs7xFt0GTFHVrSJS6Auq6mvAawCdO3e27WdjzAnLq/W5MLkOo2avZ/TX3/P5ql3c3qspN511KgnxsX6PGBLEj2oJEVkD9FTVnSJSF5itqi2KeMxYYDLuGM48VW3iLU8Cpqlqa+/r84EXgXNU9Udv2QSgB5ALVATKAKNU9XjHngAXQCkpKSf/Ro0xBtiSfoj/m5LGtFW7SKxWjr9f0ooLk+tQ1C/E4UpEFqlq56LW82sX3KfAYO/zwcDEY1cQkWoiUtb7vCbQHUgF9gJVRKS5t2pvIM1brwPwKtAvL3wAVPU6VU1S1UbAfcD4osLHGGMCJalGeV4Z2IkJQ06nQpk4bnl7sdX64F8APQn0FpF1uAB5EkBEOovI6946rYAUEVkGzAKeVNVUVc0GhgL/8743kF+P7fwLt4XzwbGnbhtjjN+s1ue3fNkFFy5sF5wxJlh+PnSU52as4615m6lYNo67z2/Gdd0aEh8b/v0Aob4LzhhjolrV8mUY3i+ZqXf24LT6VRg+KZU+z0dXrY8FkDHG+Kh57Uq8dVNXXhvYiSPZv9b6bPop8mt9LICMMcZnIsIFyXWYfs+vtT4XPBv5tT4WQMYYEyLKxhVc6/NBytaIrPWxADLGmBBzbK3P/R8uj8haHwsgY4wJUXm1Ps9e045d+yKv1scCyBhjQlhMjHB5h0Rm3tuT23s14bMVO+n1zGxemrmOzKwcv8crEQsgY4wJAxXKxnH/hS2Zcfc5nNP8FJ75Yi3nj5zDtJU7CdfrOS2AjDEmjOTV+ryTr9bn2tHzSdsZfrU+FkDGGBOGzsxX65O2ax+XvPA1f/9kBXvCqNbHAsgYY8JUXGwMA89oxOz7ejLojEa8u2ArPf81izHffk9WTq7f4xXJAsgYY8Jc/lqftolV+YdX6/P1utC+qaYFkDHGRIi8Wp/RgzpzNCeXgW8sYMi40K31sQAyxpgIIiL0bl2bL+4+mwcvasncDaFb62MBZIwxEahsXCy39mzCrPt60q99aNb6WAAZY0wEq1U5gWeucrU+idVcrc9lo75l0Wb/a30sgIwxJgq0b1CV/93ian1+2JfJH/7jf62PBZAxxkSJ/LU+d/Rq6nutjwWQMcZEmQpl47jvwha/q/WZuqJ0a30sgIwxJkodW+tz64TSrfWxADLGmCj3S63PZW1+qfV55JOVQd8aigvqsxtjjAkLcbExDOzWkL5t6/LcjHUcyc5BRIL7mkF9dmOMMWElr9anNI4F2S44Y4wxvxPsrR+wADLGGOMTCyBjjDG+sAAyxhjjCwsgY4wxvrAAMsYY4wsLIGOMMb6wADLGGOMLKc3iuXAjIruBzSV4iprATwEaJ1xE23uOtvcL9p6jRUnec0NVPaWolSyAgkhEUlS1s99zlKZoe8/R9n7B3nO0KI33bLvgjDHG+MICyBhjjC8sgILrNb8H8EG0vedoe79g7zlaBP092zEgY4wxvrAtIGOMMb6wACohEXlTRH4UkZXH+b6IyAsisl5ElotIx9KeMdCK8Z6v897rchH5TkTalfaMgVbUe863XhcRyRGRK0trtmAozvsVkZ4islREVonInNKcLxiK8d91FRGZJCLLvPf8p9KeMdBEpIGIzBKRNO893VnAOkH7GWYBVHJjgYsK+f7FQDPvYxjwn1KYKdjGUvh7/h44R1XbAv8kMvafj6Xw94yIxAJPAZ+XxkBBNpZC3q+IVAVGAf1UNRm4qpTmCqaxFP5vfDuQqqrtgJ7Av0WkTCnMFUzZwL2q2groBtwuIq2PWSdoP8MsgEpIVb8C9hSySn9gvDrzgKoiUrd0pguOot6zqn6nqnu9L+cBiaUyWBAV498Z4M/A/4Afgz9RcBXj/V4LfKSqW7z1o+E9K1BJ3J3aKnrrZpfGbMGiqjtVdbH3+X4gDah/zGpB+xlmARR89YGt+b7exu//gSPZTcBUv4cINhGpD1wOvOL3LKWkOVBNRGaLyCIRGeT3QKXgJaAVsANYAdypqrn+jhQ4ItII6ADMP+ZbQfsZFheIJzGFKui+tlFx6qGI9MIF0Fl+z1IKngMeVNWc0riVcQiIAzoB5wHlgLkiMk9V1/o7VlBdCCwFzgWaANNF5GtV3efvWCUnIhVxW+93FfB+gvYzzAIo+LYBDfJ9nYj7DSqiiUhb4HXgYlVN93ueUtAZeM8Ln5pAHxHJVtVP/B0raLYBP6nqQeCgiHwFtAMiOYD+BDyp7tqV9SLyPdASWODvWCUjIvG48Jmgqh8VsErQfobZLrjg+xQY5J1J0g3IUNWdfg8VTCKSBHwEDIzw34h/oaqnqmojVW0EfAjcFsHhAzAR6CEicSJSHjgdd/wgkm3BbfEhIrWBFsBGXycqIe941htAmqqOPM5qQfsZZltAJSQi7+LOiKkpItuAx4B4AFV9BZgC9AHWA4dwv0WFtWK850eBGsAob4sgO9yLHIvxniNKUe9XVdNEZBqwHMgFXlfVQk9RD3XF+Df+JzBWRFbgdks9qKrh3pDdHRgIrBCRpd6yvwFJEPyfYdaEYIwxxhe2C84YY4wvLICMMcb4wgLIGGOMLyyAjDHG+MICyBhjjC8sgIwxxvjCAsiYECUiN4jISwF6rk0iUrMY6x0IxOsZUxwWQMYEmHfFuP2/ZUwR7H8SYwJARBp5N/UaBSwGBorIXBFZLCIfeGWPiEgfEVktIt94N/maXMzn7ysi80VkiYjM8KpgEJHhIjJORL7wtnKuEJGnRWSFiEzzer7y3C8iC7yPpt7jT/XmXCgi/8z3ehVF5Etv/hUi0j9gf1nGeCyAjAmcFsB4oDeuBfx8Ve0IpAD3iEgC8CquoPUs4JQTeO5vgG6q2gF4D3gg3/eaAJfg7tvyNjBLVU8DDnvL8+xT1a642wo85y17HviPqnYBduVbNxO43Ju/F+7ma1FR821KjwWQMYGz2bthVzegNfCt1681GGiIa07eqKrfe+u/ewLPnQh87vWQ3Q8k5/veVFXNwt2jJhaY5i1fATTKt967+f48w/u8e77lb+VbV4D/E5HlwAzc/V9qn8C8xhTJykiNCZyD3p8CTFfVAfm/KSIdSvDcLwIjVfVTEekJDM/3vSMAqporIln6a8FjLr/9f1yL8Xme63BbaJ1UNUtENgEJJZjfmN+xLSBjAm8e0D3fcZbyItIcWA009u48CXDNCTxnFWC79/ngk5zrmnx/zvU+/xb4o/f5dce83o9e+PTCbcEZE1C2BWRMgKnqbhG5AXhXRMp6i/+uqmtF5DZgmoj8xIndyGw48IGIbMcF3KknMVpZEZmP+8Uzb+vsTuAdEbkTd1OyPBOASSKSgrsL6OqTeD1jCmW3YzCmFIlIRVU94B3QfxlYp6rP+j2XMX6wXXDGlK6h3okJq3C7uV71eR5jfGNbQMb4TET+hNsVlt+3qnq7H/MYU1osgIwxxvjCdsEZY4zxhQWQMcYYX1gAGWOM8YUFkDHGGF9YABljjPHF/wf5FHZEb5muHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#summarize results\n",
    "print(\"Best: %f using %s\" % (gsearch5_2.best_score_, gsearch5_2.best_params_))\n",
    "test_means = gsearch5_2.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch5_2.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch5_2.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch5_2.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch5_2.cv_results_).to_csv('my_preds_reg_alpha_reg_lambda_2.csv')\n",
    "\n",
    "# plot results\n",
    "test_scores = np.array(test_means).reshape(len(reg_alpha), len(reg_lambda))\n",
    "train_scores = np.array(train_means).reshape(len(reg_alpha), len(reg_lambda))\n",
    "\n",
    "#log_reg_alpha = [0,0,0,0]\n",
    "#for index in range(len(reg_alpha)):\n",
    "#   log_reg_alpha[index] = math.log10(reg_alpha[index])\n",
    "    \n",
    "for i, value in enumerate(reg_alpha):\n",
    "    pyplot.plot(reg_lambda, test_scores[i], label= 'reg_alpha:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'reg_lambda' )                                                                                                      \n",
    "pyplot.ylabel( '-Log Loss' )\n",
    "pyplot.savefig( 'reg_alpha_vs_reg_lambda1.png' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对比两次网格搜索,alpha=1.5,lambda=2时log loss最小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
